arXiv AI

From Truncation to Commitment: Persistent Context in Uniform Discrete Diffusion

The paper introduces committed reveal sampling (CRS), a training‑free sampler for uniform discrete diffusion models that stores selected argmax tokens as persistent context for subsequent predictions. CRS keeps these tokens visible in later model inputs, which theoretically prevents Bayes error from increasing as noise decreases and encourages consistent sequence‑level choices. Empirical tests on Duo‑distilled data show that CRS without top‑p truncation achieves lower generative perplexity than fixed‑p baselines across various numbers of function evaluations, offering a more favorable perplexity–entropy trade‑off.

arXiv Computation and Language

Oct 5

From Positionwise Confidence to Prefix Scheduling: Verifier Skipping in Speculative Decoding

The paper explores a new lossy strategy for speculative decoding called verifier skipping, which allows certain draft prefixes to be committed without invoking the verifier. It investigates which confidence signals—raw token confidence, learned marginal survival, or learned conditional survival—best schedule these skips, finding that raw confidence often performs best despite higher positionwise AUROC for learned signals. Experiments on HumanEval with DiffuCoder-7B-Instruct and Qwen3-32B show that all three signals can reduce verifier calls by 9.6% to 13.5% while maintaining the same pass@1 as Strict SDD.

By Haoxuan Luo, Jameson Sandler, Ferdinando Fioretto

diffusion benchmarks

arXiv Machine Learning

Jun 11

Teaching Diffusion to Speculate Left-to-Right

arXiv:2606. 11552v1 Announce Type: cross Abstract: Large language models (LLMs) achieve remarkable performance across a wide range of tasks, but their autoregressive decoding process incurs substantial inference costs due to inherently sequential token generation.

By Lexington Whalen, Yuki Ito, Ryo Sakamoto

llms diffusion benchmarks safety

arXiv Machine Learning

Jun 2

BlockGen: Flexible Blockwise Sequence Modeling with Hybrid Samplers

arXiv:2606. 02241v1 Announce Type: new Abstract: Is the uniform-state diffusion framework a more powerful paradigm for discrete diffusion?

By Justin Deschenaux, Caglar Gulcehre

diffusion

arXiv Machine Learning

Sep 1

Ceiling-Clipped Acceptance Histograms Indicate Stranded Speed-up in Block-Diffusion Speculative Decoding

arXiv:2608.30427v1 Announce Type: cross Abstract: Speculative decoding speeds up generation with an efficient draft model (drafter) that proposes tokens for a target model to verify in one pass, pres...

By Ephrem Wu

diffusion fine-tuning benchmarks

arXiv AI

Aug 7

Answer First, Reason Later: Commitment Order in Diffusion LLMs

arXiv:2608. 05687v1 Announce Type: cross Abstract: Masked diffusion language models (dLLMs) can commit tokens in any order -- a freedom marketed as their core advantage over autoregressive decoding.

By Jewon Yeom, Jaewon Sok, Seonghyeon Park, Jeongjae Park, Hwiyeong Lee, Taesup Kim

llms diffusion

arXiv AI

Jul 28

UNIFUSION: Adapting Autoregressive Language Models into Discrete Diffusion under a Unified Reverse-Rate Objective

arXiv:2607. 24507v1 Announce Type: cross Abstract: Existing methods mainly adapt pretrained autoregressive (AR) language models to masked diffusion, whereas we directly adapt them to uniform-noise diffusion, where every token remains editable during sampling.

By Xiaoyi Jiang, Jingyuan Li, Yixuan Jiang, Wei Liu, Yi Zhu, Zuoqiang Shi, Pipi Hu

llms diffusion

arXiv Machine Learning

Oct 8

CoDR: Training-Free Confidence-Drift Remasking for Diffusion Language Models

CoDR (Confidence Drift Remasking) is a training‑free, sampler‑agnostic refinement pass for masked diffusion language models that detects and corrects early token commitments that become unsupported as context grows denser. It estimates confidence drift for all committed positions using only a few forward passes and remasks only the tokens whose confidence has dropped, thereby improving accuracy across multiple backbones, tasks, and samplers with modest computational overhead.

By Yue Wu, Qinghe Zhang, Yu Zhang, Jian Huang

llms diffusion

arXiv Computation and Language

Oct 1

DEdit: Iterative Draft Editing for Speculative Decoding

arXiv:2609.38510v1 Announce Type: new Abstract: Speculative decoding accelerates autoregressive LLMs by having a lightweight drafter propose tokens that the target model verifies in parallel. Diffusi...

By Longxuan Yu, Bingsen Chen, Peng Shi, Dongkyu Lee, Yi Xiang, Hideo Kobayashi, Sheng Zhang, Shuaichen Chang, Xing Niu, Zhuoyan Xu, Greg Ver Steeg, Jiarong Jiang

llms diffusion benchmarks

Hugging Face Trending Papers

Jun 10

Teaching Diffusion to Speculate Left-to-Right

Large language models (LLMs) achieve remarkable performance across a wide range of tasks, but their autoregressive decoding process incurs substantial inference costs due to inherently sequential token generation. Speculative decoding addresses this bottleneck by employing a lightweight draft model to propose multiple future tokens that are subsequently verified in parallel by a larger target model.

llms diffusion benchmarks safety

arXiv Machine Learning

Jun 15

Neither Parallel Nor Sequential: How DiffusionGemma Actually Commits Tokens

arXiv:2606. 14620v1 Announce Type: new Abstract: Open diffusion language models are marketed as parallel, non-autoregressive decoders, yet the order in which a shipped checkpoint actually commits its tokens is almost never measured.

By Ali Asaria, Tony Salomone, Deep Gandhi

llms diffusion safety

arXiv AI

Sep 15

Carryover Drafting: Recycling Rejected States for Speculative Decoding

arXiv:2609.14717v1 Announce Type: cross Abstract: Speculative decoding accelerates LLM inference by verifying multiple drafted tokens in parallel, allowing a single target forward pass to accept seve...

By Jahyun Koo, Sunghyeon Woo, Jaeeun Kil, Jeongtae Lee, Sungjae Lee, Kyomin Jung, Minsub Kim

llms rag

arXiv Machine Learning

Jun 18

Decomposing Prediction Mechanisms for In-Context Recall

arXiv:2507. 01414v2 Announce Type: replace Abstract: We introduce a new family of toy problems that combine features of linear-regression-style continuous in-context learning (ICL) with discrete associative recall.

By Sultan Daniels, Dylan Davis, Dhruv Gautam, Wentinn Liao, Gireeja Ranade, Anant Sahai

llms efficiency

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